• DocumentCode
    2852930
  • Title

    Optimization of multi periods Inventory Routing Problem model with time varying demand

  • Author

    Moin, Noor Hasnah

  • Author_Institution
    Inst. of Math. Sci., Univ. of Malaya, Kuala Lumpur, Malaysia
  • fYear
    2011
  • fDate
    6-9 Dec. 2011
  • Firstpage
    190
  • Lastpage
    194
  • Abstract
    In this paper we consider a multi period Inventory Routing Problem (IRP) that faces time varying demand of multi product from the assembly plant. The problem addressed in this study is a many-to-one distribution network consisting of an assembly plant and many geographically dispersed suppliers where each supplier supplies distinct product to the assembly plant. It is based on a finite horizon, multi-periods, multi-suppliers and multi-products where a fleet of capacitated homogeneous vehicles, housed at a depot, transport parts from the suppliers to meet the demand specified b y the assembly plant in each period. We propose a solution method based on the Variable Neighborhood Search. The algorithm incorporates the Generalised Insertion (GENI) method and the algorithm are run on several problems from the literature and the results are compared with the Genetic Algorithms. VNS performs better on larger problems.
  • Keywords
    assembling; genetic algorithms; inventory management; search problems; supply and demand; assembly plant; capacitated homogeneous vehicles; generalised insertion method; genetic algorithms; many-to-one distribution network; multiperiod inventory routing problem model; multiproducts; multisuppliers; time varying product demand; variable neighborhood search; Assembly; Genetic algorithms; Optimization; Planning; Routing; Vehicles; Genetic Algorithm; Inbound Logistics; Inventory Routing; Variable Neighborhood Search;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
  • Conference_Location
    Singapore
  • ISSN
    2157-3611
  • Print_ISBN
    978-1-4577-0740-7
  • Electronic_ISBN
    2157-3611
  • Type

    conf

  • DOI
    10.1109/IEEM.2011.6117905
  • Filename
    6117905